{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.pipeline import make_pipeline\n",
    "print(__doc__)\n",
    "\n",
    "# Code source: Tyler Lanigan <tylerlanigan@gmail.com>\n",
    "#              Sebastian Raschka <mail@sebastianraschka.com>\n",
    "\n",
    "# License: BSD 3 clause\n",
    "# slight modifications by Felipe Almeida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction accuracy for the normal test dataset with PCA\n",
      "81.48%\n",
      "\n",
      "\n",
      "Prediction accuracy for the standardized test dataset with PCA\n",
      "98.15%\n",
      "\n",
      "\n",
      "PC 1 without scaling:\n",
      " [  1.76342917e-03  -8.35544737e-04   1.54623496e-04  -5.31136096e-03\n",
      "   2.01663336e-02   1.02440667e-03   1.53155502e-03  -1.11663562e-04\n",
      "   6.31071580e-04   2.32645551e-03   1.53606718e-04   7.43176482e-04\n",
      "   9.99775716e-01]\n",
      "\n",
      "PC 1 with scaling:\n",
      " [ 0.13443023 -0.25680248 -0.0113463  -0.23405337  0.15840049  0.39194918\n",
      "  0.41607649 -0.27871336  0.33129255 -0.11383282  0.29726413  0.38054255\n",
      "  0.27507157]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5e3cce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x5eXlKZ1nR0cHJSUlKZ1nrin0dSz09YPCX8dCXz/QOhaCQl8/6L2Ozz777E7n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\nmZmZ2ZDxP8C7J7hLVZONAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5e4ca470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RANDOM_STATE = 42\n",
    "FIG_SIZE = (10, 7)\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "features, target = load_wine(return_X_y=True)\n",
    "\n",
    "# Make a train/test split using 30% test size\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target,\n",
    "                                                    test_size=0.30,\n",
    "                                                    random_state=RANDOM_STATE)\n",
    "\n",
    "# Fit to data and predict using pipelined GNB and PCA.\n",
    "unscaled_clf = make_pipeline(PCA(n_components=2), GaussianNB())\n",
    "unscaled_clf.fit(X_train, y_train)\n",
    "pred_test = unscaled_clf.predict(X_test)\n",
    "\n",
    "# Fit to data and predict using pipelined scaling, GNB and PCA.\n",
    "std_clf = make_pipeline(StandardScaler(), PCA(n_components=2), GaussianNB())\n",
    "std_clf.fit(X_train, y_train)\n",
    "pred_test_std = std_clf.predict(X_test)\n",
    "\n",
    "# Show prediction accuracies in scaled and unscaled data.\n",
    "print('\\nPrediction accuracy for the normal test dataset with PCA')\n",
    "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test)))\n",
    "\n",
    "print('\\nPrediction accuracy for the standardized test dataset with PCA')\n",
    "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test_std)))\n",
    "\n",
    "# Extract PCA from pipeline\n",
    "pca = unscaled_clf.named_steps['pca']\n",
    "pca_std = std_clf.named_steps['pca']\n",
    "\n",
    "# Show first principal componenets\n",
    "print('\\nPC 1 without scaling:\\n', pca.components_[0])\n",
    "print('\\nPC 1 with scaling:\\n', pca_std.components_[0])\n",
    "\n",
    "# Scale and use PCA on X_train data for visualization.\n",
    "scaler = std_clf.named_steps['standardscaler']\n",
    "X_train_std = pca_std.transform(scaler.transform(X_train))\n",
    "\n",
    "# visualize standardized vs. untouched dataset with PCA performed\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=FIG_SIZE)\n",
    "\n",
    "\n",
    "for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):\n",
    "    ax1.scatter(X_train[y_train == l, 0], X_train[y_train == l, 1],\n",
    "                color=c,\n",
    "                label='class %s' % l,\n",
    "                alpha=0.8,\n",
    "                marker=m\n",
    "                )\n",
    "\n",
    "for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):\n",
    "    ax2.scatter(X_train_std[y_train == l, 0], X_train_std[y_train == l, 1],\n",
    "                color=c,\n",
    "                label='class %s' % l,\n",
    "                alpha=0.8,\n",
    "                marker=m\n",
    "                )\n",
    "\n",
    "ax1.set_title('Raw training dataset after PCA',fontsize=20)\n",
    "ax2.set_title('Standardized training dataset after PCA',fontsize=20)\n",
    "\n",
    "for ax in (ax1, ax2):\n",
    "    ax.set_xlabel('1st principal component',fontsize=15)\n",
    "    ax.set_ylabel('2nd principal component',fontsize=15)\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.grid()\n",
    "\n",
    "fig.set_size_inches(15,7.5)\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Global TF Kernel (Python 3)",
   "language": "python",
   "name": "global-tf-python-3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
